{"id":"W2976785851","doi":"10.1101/786251","title":"Computationally guided high-throughput design of self-assembling drug nanoparticles","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Pharmacological Effects of Natural Compounds","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Cancer Institute; National Institutes of Health; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Skolkovo Institute of Science and Technology; Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology; Pharmaceutical Research and Manufacturers of America Foundation","keywords":"Drug; Nanotechnology; Drug discovery; Nanoparticle; Computer science; Pharmacology; Chemistry; Materials science; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004166575,0.0004477928,0.0005391527,0.000280708,0.0003060837,0.0005624549,0.0004881083,0.0005722779,0.00138546],"category_scores_gemma":[0.000682619,0.000395864,0.0003996893,0.0001544255,0.0003293445,0.0003222807,0.0003413102,0.0004368465,0.0003311422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001090514,"about_ca_system_score_gemma":0.001094828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002431211,"about_ca_topic_score_gemma":0.003282173,"domain_scores_codex":[0.9998889,0.00001835915,0.000004653549,0.00002381224,0.00003435492,0.00002990868],"domain_scores_gemma":[0.9997621,0.0001193324,0.00003327187,0.00001429429,0.00004782284,0.00002318979],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001046761,0.0001251851,0.0006410415,0.00008578582,0.0000251484,0.00008737965,0.00002689614,0.9399411,0.04483265,0.003428206,0.0005553243,0.01014655],"study_design_scores_gemma":[0.00001297422,0.00003002383,0.00005127968,0.000001116584,0.000002307761,0.000003509524,0.000004264568,0.9937709,0.005672785,0.0002280102,0.0002205386,0.000002190057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5562019,0.0004191325,0.427396,0.0003508876,0.000103336,0.000405359,0.0002788882,0.001447409,0.01339692],"genre_scores_gemma":[0.8652572,0.0001011347,0.1320682,0.00005645605,0.000009497385,0.0002736714,0.0001722519,0.0001368618,0.001924678],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002431211,"threshold_uncertainty_score":0.007912278,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0786547346811777,"score_gpt":0.3604020117162917,"score_spread":0.2817472770351139,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}